OTSUHARA-WATH Filter for Poisson Noise Removal in Low Light Condition Digital Image

Authors

  • Suhaila Sari Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor,Malaysia
  • Nurul Faziha Azlan Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor,Malaysia
  • Hazli Roslan Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor,Malaysia
  • N.S.A.M. Tajuddin EmbCoS Research Focus Group, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor,Malaysia

Keywords:

Kuwahara Filter, Low Light, Noise Removal, Otsu Threshold, Wavelet Threshold,

Abstract

Nowadays, the digital images are used widely due to the development of sophisticated technologies. The recent device that is very popular among its users related to digital images is smartphone. This is due to nowadays smartphone is embedded with its own camera that can capture digital images. Nevertheless, the digital image is easily exposed to various types of noise, especially the Poisson noise in low light condition. Therefore, this study aims to develop a new denoising technique for Poisson noise removal in low light condition digital images. This study proposes a denoising method named as OTSUHARAWATH Filter, which utilizes the Otsu Threshold, Kuwahara Filter and Wavelet Threshold. The proposed methods performance is evaluated based on the Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE) and visual inspection. The comparison between the proposed methods and the existing denoising methods is also performed. From the results of PSNR, MSE, computational time and visual inspection, it can be proven that the OTSUHARA-WATH Filter is able to reduce and smooth noise, while preserving the edges and fine details of the image at low and medium level of Poisson noise in comparison to the existing methods.

References

F. Alter, Y. Matsushita, X. Tang. 2006. An intensity similarity measure in low-light conditions. Lecture Notes in Computer Science. 3954:267.

How to get better digital photos in low light conditions without using a flash retrieved September 22, 2014 from http://digital-photographyschool.com/how-to-getbetter-digital-photos-in-low-light conditions without-using-a-flash/.

Challenging photography at night safari retrieved. September 25, 2014 from http://jefzlim.blogspot.com /2013/11/challenging- photography-atnight safari.html

B.K.S. Kumar. 2012. Image denosing based on local mean filter and its method thresholding. Springer Signal, Image and Video Processing Journal.

T. C. Lin, C. T. Yeh, M. K. Liu. 2010. Application of SVM based filter

using LMS learning algorithm for image denoising. Lecture Notes in Computer Science. 6444:82-90.

J. Muller. 2013. Bregmanized (higher order) total variation and application in PET. Westafalische Wilhems Universitat,Munster.

J.J. Francis, G. de Jager. 2008. The bilateral median filter. IEEE Transactions on Image Processing. 421-432.

C.Tomasi, R. Manduchi. 1998. Bilateral filtering for gray and color images. IEEE International Conference on Computer Vision. 839-846.

K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, 2007. Image denoising by sparse 3d transform domain collaborative filtering. IEEE Transaction on Image Processing. 16(8): 2080-2095.

H. Hassaan, A. Saparon. 2011. Still image denoising based on discrete wavelet transform. IEEE International Conference on System Engineering and Technology. 188-191.

S. Suhaila, S. Z. H. A. Fakkri, R. Hazli, Z. Tukiran. 2013. Development of denoising method for digital image in low light condition. IEEE International Conference on Control System, Computing and Engineering. 142-147.

L. F. Shiung. 2014. Pembangunan teknik nyah-hingar hibrid untuk imej digital dalam keadaan cahaya malap. Universiti Tun Hussein Onn Malaysia.

P. Bakker, L.J. V. van Vliet, P.W. Verbeek. 1999. Edge preserving orientation adaptive filtering. Computer Vision and Pattern Recognition.1:540.

D. L. Donoho. 1995. Denoising by soft thresholding. IEEE Transactions on Information Theory, 41(3): 613-627.

S.G. Chang. B.Yu, M. Vetterli. 2000. Adaptive wavelet threshold for image denoising and compression. IEEE Transaction on Image Processing. 9(9): 1532-1546.

K. Benoit. 2011. Linear regression models with logarithmic

transformations. Methodology Institute London School of Economics.

Downloads

Published

2016-07-01

How to Cite

Sari, S., Azlan, N. F., Roslan, H., & Tajuddin, N. (2016). OTSUHARA-WATH Filter for Poisson Noise Removal in Low Light Condition Digital Image. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(4), 121–126. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1185

Issue

Vol. 8 No. 4: Pioneering Future Innovation Through Green Technology II

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.